EXPLORING THE ROLE OF EVIDENCE-BASED NURSING PRACTICES IN ENHANCING POSTOPERATIVE RECOVERY AND PATIENT SAFETY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Postoperative recovery remains a critical period where patients are vulnerable to complications and prolonged healing. While evidence-based nursing (EBN) practices are advocated to standardize care and improve outcomes, a comprehensive synthesis of their collective impact on postoperative recovery and patient safety is needed. Objective: This systematic review aimed to evaluate the impact of evidence-based nursing practices on postoperative recovery metrics and patient safety outcomes in adult patients undergoing elective surgery. Methods: A systematic review was conducted following PRISMA guidelines. Databases including PubMed, Scopus, Cochrane Library, and CINAHL were searched for randomized controlled trials and observational studies published between 2014 and 2024. Inclusion criteria focused on studies comparing structured EBN interventions to usual care. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment using the Cochrane RoB 2 and Newcastle-Ottawa tools. A narrative synthesis was performed. Results: Eight studies (n=4,217 patients) were included. The evidence consistently demonstrated that EBN practices, such as protocol-driven mobilization and complication-specific care bundles, significantly improved key outcomes. These interventions were associated with a reduced length of hospital stay, lower incidence of major complications including surgical site infections and pneumonia, and decreased 30-day readmission rates. The results were statistically significant across multiple surgical specialties. Conclusion: The implementation of evidence-based nursing practices is a fundamental and effective strategy for enhancing postoperative recovery and ensuring patient safety. These findings provide a robust justification for the standardization of nursing care around proven protocols. Future research should focus on standardizing intervention definitions and conducting economic evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".